Question: Bioconductor packages for machine learning with RNAseq
1
gravatar for jonessara770
2.7 years ago by
jonessara77010
jonessara77010 wrote:

Hello all,

can you please give a list of packages that have been developed to apply machine learning models on RNAseq data?

 

Thanks

Sara

bioconductor • 481 views
ADD COMMENTlink modified 2.7 years ago by Wolfgang Huber13k • written 2.7 years ago by jonessara77010

There is a bioconductor package "MLInterfaces" that contains interfaces to R machine learning procedures such as svm, random forest, knn, and etc.  Those procedures are built for general purpose, yet you might find it quite helpful for expression data.

ADD REPLYlink written 2.7 years ago by Chao-Jen Wong30
Answer: Bioconductor packages for machine learning with RNAseq
1
gravatar for James W. MacDonald
2.7 years ago by
United States
James W. MacDonald50k wrote:

If you go to the package listing page, on the left hand side are a set of 'biocViews', one of which is StatisticalMethod. Machine learning is a broad category that encompasses several of the methods there, but you should be able to look through the choices and see which package(s) are applicable.

ADD COMMENTlink written 2.7 years ago by James W. MacDonald50k
Answer: Bioconductor packages for machine learning with RNAseq
1
gravatar for Wolfgang Huber
2.7 years ago by
EMBL European Molecular Biology Laboratory
Wolfgang Huber13k wrote:

Sara

perhaps you are referring to characteristics of RNA-Seq data that make it necessary to "preprocess" the data before applying regular ML methods: normalisation for library size and transformation to make the data less skewed and less heteroskedastic. Have a look at the variance stabilizing transformation in DESeq2. It's also described in the vignette.

Hope this helps

Wolfgang

 

ADD COMMENTlink written 2.7 years ago by Wolfgang Huber13k
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